ConsistentFakeEmbeddings Class — langchain Architecture

Architecture documentation for the ConsistentFakeEmbeddings class in fake_embeddings.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  d3609afb_32d8_29f5_39d7_a97fc460c871["ConsistentFakeEmbeddings"]
  dca08b34_f922_a3fa_4752_e72b03b86d89["FakeEmbeddings"]
  d3609afb_32d8_29f5_39d7_a97fc460c871 -->|extends| dca08b34_f922_a3fa_4752_e72b03b86d89
  b656da5c_781f_2669_c8cd_83d309b3feb8["fake_embeddings.py"]
  d3609afb_32d8_29f5_39d7_a97fc460c871 -->|defined in| b656da5c_781f_2669_c8cd_83d309b3feb8
  b45a6e68_5f94_600e_aac0_4c435ce833f9["__init__()"]
  d3609afb_32d8_29f5_39d7_a97fc460c871 -->|method| b45a6e68_5f94_600e_aac0_4c435ce833f9
  83a52020_7802_533b_0bc5_92c500327050["embed_documents()"]
  d3609afb_32d8_29f5_39d7_a97fc460c871 -->|method| 83a52020_7802_533b_0bc5_92c500327050
  424b48f2_1163_8b09_3d82_077893289663["embed_query()"]
  d3609afb_32d8_29f5_39d7_a97fc460c871 -->|method| 424b48f2_1163_8b09_3d82_077893289663

Relationship Graph

Source Code

libs/langchain_v1/tests/integration_tests/cache/fake_embeddings.py lines 39–68

class ConsistentFakeEmbeddings(FakeEmbeddings):
    """Consistent fake embeddings.

    Fake embeddings which remember all the texts seen so far to return consistent
    vectors for the same texts.
    """

    def __init__(self, dimensionality: int = 10) -> None:
        self.known_texts: list[str] = []
        self.dimensionality = dimensionality

    def embed_documents(self, texts: list[str]) -> list[list[float]]:
        """Return consistent embeddings for each text seen so far."""
        out_vectors = []
        for text in texts:
            if text not in self.known_texts:
                self.known_texts.append(text)
            vector = [1.0] * (self.dimensionality - 1) + [
                float(self.known_texts.index(text)),
            ]
            out_vectors.append(vector)
        return out_vectors

    def embed_query(self, text: str) -> list[float]:
        """Return consistent embeddings.

        Return consistent embeddings for the text, if seen before, or a constant
        one if the text is unknown.
        """
        return self.embed_documents([text])[0]